1st Edition

Fundamentals of Systems Biology From Synthetic Circuits to Whole-cell Models

By Markus W. Covert Copyright 2015
    368 Pages 120 B/W Illustrations
    by CRC Press

    368 Pages
    by CRC Press

    For decades biology has focused on decoding cellular processes one gene at a time, but many of the most pressing biological questions, as well as diseases such as cancer and heart disease, are related to complex systems involving the interaction of hundreds, or even thousands, of gene products and other factors. How do we begin to understand this complexity?

    Fundamentals of Systems Biology: From Synthetic Circuits to Whole-cell Models introduces students to methods they can use to tackle complex systems head-on, carefully walking them through studies that comprise the foundation and frontier of systems biology. The first section of the book focuses on bringing students quickly up to speed with a variety of modeling methods in the context of a synthetic biological circuit. This innovative approach builds intuition about the strengths and weaknesses of each method and becomes critical in the book’s second half, where much more complicated network models are addressed—including transcriptional, signaling, metabolic, and even integrated multi-network models.

    The approach makes the work much more accessible to novices (undergraduates, medical students, and biologists new to mathematical modeling) while still having much to offer experienced modelers--whether their interests are microbes, organs, whole organisms, diseases, synthetic biology, or just about any field that investigates living systems.

    Variations on a Theme of Control
    Learning Objectives
    Our Theme: A Typical Negative Autoregulatory Circuit
    Recommended Reading
    Variation: Boolean Representations
    Learning Objectives
    Boolean Logic and Rules
    State Matrices
    State Transitions
    Advantages and Disadvantages of Boolean Analysis
    Recommended Reading
    Variation: Analytical Solutions of Ordinary Differential Equations
    Learning Objectives
    Synthetic Biological Circuits
    From Compartment Models to ODEs
    Specifying and Simplifying ODEs with Assumptions
    The Steady-State Assumption
    Solving the System without Feedback: Removal of Activator
    Key Properties of the System Dynamics
    Solving the System without Feedback: Addition of Activator
    Comparison of Modeling to Experimental Measurements
    Addition of Autoregulatory Feedback
    Comparison of the Regulated and Unregulated Systems
    Recommended Reading
    Variation: Graphical Analysis
    Learning Objectives
    Revisiting the Protein Synthesis ODEs
    Plotting X versus dX/dt
    Fixed Points and Vector Fields
    From Vector Fields to Time-Course Plots
    Bifurcation Analysis
    Adding Feedback
    Two-Equation Systems
    Recommended Reading
    Variation: Numerical Integration
    Learning Objectives
    The Euler Method
    Accuracy and Error
    The Midpoint Method
    The Runge-Kutta Method
    Recommended Reading
    Variation: Stochastic Simulation
    Learning Objectives
    Single Cells and Low Molecule Numbers
    Stochastic Simulations
    The Probability that Two Molecules Interact and React in a Given Time Interval
    The Probability of a Given Molecular Reaction Occurring over Time
    The Relationship between Kinetic and Stochastic Constants
    Gillespie's Stochastic Simulation Algorithm
    Stochastic Simulation of Unregulated Gene Expression
    Stochastic Simulations versus Other Modeling Approaches
    Recommended Reading
    Transcriptional Regulation
    Learning Objectives
    Transcriptional Regulation and Complexity
    More Complex Transcriptional Circuits
    The Transcriptional Regulatory Feed-Forward Motif
    Boolean Analysis of the Most Common Internally Consistent Feed-Forward Motif Identified in E. coli
    An ODE-Based Approach to Analyzing the Coherent Feed-Forward Loop
    Robustness of the Coherent Feed-Forward Loop
    Experimental Interrogation of the Coherent Feed-Forward Loop
    Changing the Interaction from an AND to an OR Relationship
    The Single-Input Module
    Just-in-Time Gene Expression
    Generalization of the Feed-Forward Loop
    An Example of a Multigene Feed-Forward Loop: Flagellar Biosynthesis in E. coli
    Other Regulatory Motifs
    Recommended Reading
    Signal Transduction
    Learning Objectives
    Receptor-Ligand Binding to Form a Complex
    Application to Real Receptor-Ligand Pairs
    Formation of Larger Complexes
    Protein Localization
    The NF-kB Signaling Network
    A Detailed Model of NF-kB Activity
    Alternative Representations for the Same Process
    Specifying Parameter Values from Data
    Bounding Parameter Values
    Model Sensitivity to Parameter Values
    Reducing Complexity by Eliminating Parameters
    Parameter Interactions
    Recommended Reading
    Learning Objectives
    Cellular Metabolism
    Metabolic Reactions
    Compartment Models of Metabolite Concentration
    The Michaelis-Menten Equation for Enzyme Kinetics
    Determining Kinetic Parameters for the Michaelis-Menten System
    Incorporating Enzyme Inhibitory Effects
    Flux Balance Analysis
    Steady-State Assumption and Exchange Fluxes
    Solution Spaces
    The Objective Function
    Defining the Optimization Problem
    Solving FBA Problems Using MATLAB
    Applications of FBA to Large-Scale Metabolic Models
    Using FBA for Metabolic Engineering
    Recommended Reading
    Integrated Models
    Learning Objectives
    Dynamic FBA: External versus Internal Concentrations
    Environmental Constraints
    Integration of FBA Simulations over Time
    Comparing Dynamic FBA to Experimental Data
    FBA and Transcriptional Regulation
    Transcriptional Regulatory Constraints
    Regulatory FBA: Method
    REGULATORY FBA: Application
    Toward Whole-Cell Modeling
    Recommended Reading


    Markus Covert is an Associate Professor of Bioengineering and, by courtesy, Chemical and Systems Biology at Stanford University. He has received the National Institute of Health Director’s Pioneer Award and an Allen Distinguished Investigator Award from the Paul Allen Family Foundation. He is best known for the development of the first “whole-cell” computational model of a bacterial cell.

    "Author has excellent command of both aspects of systems biology."
    —Joel Bader, Johns Hopkins University, Baltimore, Maryland, USA

    "… an excellent introduction to systems thinking and modeling in the context of complex biological problems. … uses concrete biological examples to develop systems concepts and model step by step, thus enabling the reader to understand the power of systems biology in the study of complex biological phenomena. … develops a deep intuition of systems thinking in the context of complex biological phenomena. This intuition is then translated into concrete systems modeling approaches enabling readers to apply the systems approach to their own problems."
    —Prof Werner Dubitzky, University of Ulster